class_docstr: Runs an op when a metric has plateaued.
docstr: See EarlyStoppingHook for args. Runs plateau_op if plateaued.
docstr_tok: ["See","EarlyStoppingHook","for","args",".","Runs","plateau_op","if","plateaued","."]
code: def __init__(
      self,
      events_dir,
      tag,
      plateau_op,
      num_plateau_steps=1000,
      plateau_delta=0.1,
      plateau_decrease=True,
      every_n_steps=1000,
      only_once=False,
  ):
      """See EarlyStoppingHook for args. Runs plateau_op if plateaued."""
      super(PlateauOpHook, self).__init__(
          events_dir=events_dir, tags=[tag], every_n_steps=every_n_steps
      )
      self._num_plateau_steps = num_plateau_steps
      self._plateau_delta = plateau_delta
      self._plateau_decrease = plateau_decrease
      self._plateau_op = plateau_op
      self._only_once = only_once
      self._should_run_op = False
      self._ever_ran = False
      self._last_metric_step_seen = 0
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tensorflow.compat.v1
  from tensorboard.backend.event_processing import event_accumulator
  from tensorboard.backend.event_processing import event_multiplexer
label_desc: Use image node_id 1 to create a new PlateauOpHook object from inherited base classes: MetricsBasedHook with example: obj = PlateauOpHook(events_dir, tag, plateau_op, num_plateau_steps, plateau_delta, plateau_decrease, every_n_steps, only_once)